proTRAC Analysis

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#make differentiated counts positive again


#data for lollipop plot
lollipop <- man_clusters[man_clusters$chromosome != "MT",]
for(row in 1:nrow(lollipop)){
  lollipop$x_cord[row] <- paste(lollipop$chromosome[row], lollipop$cell_type[row], collapse = " ")
}

chromosome_celltype <- c()
for(chrom in c(as.character(1:22), "X","Y")){
 chromosome_celltype[length(chromosome_celltype) + 1] <- paste(chrom, "stem", collapse = " ")
 chromosome_celltype[length(chromosome_celltype) + 1] <- chrom
 chromosome_celltype[length(chromosome_celltype) + 1] <- paste(chrom, "diff", collapse = " ") 
}


lollipop$x_cord <- factor(lollipop$x_cord,levels = chromosome_celltype)
lollipop$start <- lollipop$start/1000000
lollipop$end <- lollipop$end/1000000

#lollipop$start_jitter <- jitter(lollipop$start,40)



#vector holding chromosome info
chromosome_info <- as.data.frame(c(248956422,242193529,198295559,190214555, 181538259, 170805979, 159345973 , 145138636, 138394717, 133797422,135086622, 133275309, 114364328, 107043718, 101991189,  90338345,  83257441,  80373285,  58617616, 64444167, 46709983 ,50818468, 156040895 ,  57227415)/1000000)
colnames(chromosome_info) <- c("chromosome_length")
chromosome_info$chromosome <- factor(c(as.character(1:22), "X","Y"), levels = levels(lollipop$x_cord))
#chromosome_info$chromosome <- c(as.character(1:22), "X","Y")

chromosome_info$centromere <- c(125,93.3,91,50.4,48.4,61,59.9,45.6,49,40.2,53.7,35.8,17.9,17.6,19,36.6,24,17.2,26.5,27.5,13.2,14.7,60.6,12.5)
# 
# chrom_info$left_width <- NA
# chrom_info$right_width <- NA
# for(row in 1:nrow(chrom_info)){
#  chrom_info$left_width[row] <- paste(chrom_info$chrom_name[row], "stem", collapse = " ")
#  chrom_info$right_width[row] <- paste(chrom_info$chrom_name[row], "diff", collapse = " ")
# 
# }
 # pdf("../../figures/piRNA_Clusters.pdf", 12,4)

  ggplot() +
     #chromosome
    geom_bar(data = chromosome_info, aes(x = chromosome, y = chromosome_length),alpha = .5, stat = "identity",width = 1) +
    geom_text(data = chromosome_info, aes( x= chromosome, y = chromosome_length + 10, label = chromosome)) +
    #cluster location bar
    geom_segment(data=lollipop,aes(x = chromosome, xend = x_cord, y = start , yend = start, color = cluster_count)) +
    #cluster location point
    geom_point(data=lollipop, aes(x = x_cord, y = start, color = cluster_count, shape = cell_type), size = 1, alpha = .5) +
    #geom_point(data = chrom_info, aes(x = chrom_name, y = centromere), color = c("white"), shape = '-', size = 20) +  
    #geom_point(data = chrom_info, aes(x = chrom_name, y = centromere), shape = '-', size = 15) +  
    #geom_segment(data = chrom_info, aes(x = chromosome, xend = x_cord, y = start, yend = start, color = cluster_count)) +    
   
    #centromere figure
    geom_tile(data = chromosome_info, aes(x = chromosome, y = centromere,height = 1, width = 1), fill = "white") +
    geom_tile(data = chromosome_info, aes(x = chromosome, y = centromere,height = 1, width = .75)) +
    ylab("Location (Mbp)") + 
    xlab("Chromosome") +
    scale_y_continuous(labels = comma, breaks = c(50,100,150,200,250,300)) +
    scale_x_discrete(limit = levels(lollipop$x_cord), breaks = NULL) +
    ggtitle("Cluster Locations on Chromosomes") +
    theme_bw() +  
    labs(color = "Frequency of Cluster", shape = "Cell Type") +
    #scale_shape_discrete(breaks=c("stem","diff"), labels = c("Stem", "Differentiated")) +
    scale_shape_manual(values = c(19, 17),breaks=c("stem","diff"), labels = c("GSC", "GBM")) +
    scale_color_continuous(breaks = c(1,2,3,4,5,6,7,8), trans = "reverse") +
    #hide the x axis information
    theme(axis.text.x = element_blank(),
          axis.ticks.x = element_blank(),
          legend.position = c(.8,.65),
          legend.background = element_blank())

          #legend.key.height =  unit(.4,"cm"),
          #legend.key.width =  unit(1,"cm"))
  
 # dev.off()